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International Bhurban Conference on applied Sciences & Technology, IBCAST

作者： Syed Ussama Ali M. Zamurad Shah Raza Samar Control And Signal Processing Research Group (CASPR) Capital University of Science and Technology Islamabad Pakistan Center of Excellence in Science and Applied Technologies (CESAT) Islamabad Pakistan Center of Excellence in Science and Applied Technologies (CESAT) Islamabad Pakistan

research unmanned air vehicles (UAVs) provide a cost effective solution for rapid development and testing of new algorithms in real system flight testing. The sliding mode control architecture is applied by the authors to the lateral dynamics of scaled YAK-54 UAV. Flight dynamic analysis and control system design is complicated by the problem of uncertain measurements, external disturbances and modeling uncertainties. Using the powerful tools of sliding mode theory we present an innovative robust lateral control algorithm. For robust stabilization of lateral dynamics, 1 st -order sliding mode control was designed. Due to its limitation of chattering in the control channel and extra information demand, a 2 nd -order super twisting algorithm was designed to provide chattering free improved robust control. The designed framework's effectiveness is tested by applying the developed scheme on scaled YAK-54 UAV. The results of the flight tests are included in the paper to demonstrate the benefits of the adopted framework for the control design and further it focuses on bridging the gap between theory and experimental practice.

关键词： Sliding mode control Algorithm design and analysis Robustness Aerodynamics Control design Vehicle dynamics

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Corrigendum to "syndetically proximal Pairs" [J. Math. Anal. Appl. 379 (2011) 656-663]

arXiv

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arXiv 2017年

作者： Li, Jian Moothathu, T.K. Subrahmonian Oprocha, Piotr Department of Mathematics Shantou University Shantou Guangdong515063 China Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Techniques Shantou University Shantou Guangdong515063 China School of Mathematics and Statistics University of Hyderabad Hyderabad500 046 India AGH University of Science and Technology Faculty of Applied Mathematics AL. Mickiewicza 30 KrakÃ³ W30-059 Poland National Supercomputing Centre IT4 Innovations Division of the University of Ostrava Institute for Research and Applications of Fuzzy Modeling 30. Dubna 22 Ostrava70103 Czech Republic

We give a counterexample to Theorem 9 in "Syndetically proximal pairs"[J. Math. Anal. Appl. 379 (2011) 656-663]. We also provide sufficient conditions for the conclusion of Theorem 9 to hold. Copyright ©... 详细信息

关键词： Optimization

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Remote oil spill detection and monitoring beneath sea ice

Remote oil spill detection and monitoring beneath sea ice

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Living Planet Symposium 2016

作者： Polak, Adam Marshall, Stephen Ren, Jinchang Hwang, Byongjun Hagan, Bernard Stothard, David J.M. Centre for Signal and Image Processing University of Strathclyde 204 George Street GlasgowG1 1XW United Kingdom Scottish Marine Institute Oban Argyll ObanPA37 1QA United Kingdom Fraunhofer Centre for Applied Photonics Fraunhofer UK Research Ltd. 99 George Street GlasgowG1 1RD United Kingdom

ISBN: (纸本)9789292213053

The spillage of oil in Polar Regions is particularly serious due to the threat to the environment and the difficulties in detecting and tracking the full extent of the oil seepage beneath the sea ice. Development of fast and reliable sensing techniques is highly desirable. In this paper hyperspectral imaging combined with signal processing and classification techniques are proposed as a potential tool to detect the presence of oil beneath the sea ice. A small sample, lab based experiment, serving as a proof of concept, resulted in the successful identification of oil presence beneath the thin ice layer as opposed to the other sample with ice only. The paper demonstrates the results of this experiment that granted a financial support to execute full feasibility study of this technology for oil spill detection beneath the sea ice.

关键词： Sea ice

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Wind estimation for lateral path following of UAVs using higher order sliding mode

Wind estimation for lateral path following of UAVs using hig...

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International Conference on Intelligent Systems Engineering (ICISE)

作者： Syed Ussama Ali M. Zamurad Shah Raza Samar Athar Waseem Control And Signal Processing Research (CASPR) Group Capital University of Science And Technology Islamabad Pakistan Center of Excellence in Science and Applied Technologies (CESAT) Islamabad Pakistan International Islamic University Islamabad Pakistan

In design of guidance algorithm's wind is often ignored or only considered implicitly. Persistent winds has a very significant nonlinear effect on the guidance scheme as for small UAVs these disturbances can strongly affect their spatial orientation. This research work extends the idea of sliding mode control for parameter estimation of UAV nonlinear dynamics. The uncertain parameter estimation scheme is designed to estimate wind based on the higher order sliding mode robust differentiator (HOSMD) using rate of change of heading of the vehicle. Further these estimates are then included in the guidance algorithm. The UAVs guidance algorithm's objective is to derive the lateral track error towards zero with graceful and stable manoeuvres and then to keep it as minimum as possible while subject to disturbing winds. In this scheme a second order sliding motion is established along designed sliding manifold and outputs the reference bank commands for improved tracking performance using estimates during curved arcs. The estimation is combined seamlessly with robust guidance algorithm to produce integrated identification and guidance scheme for lateral path following application. The combined framework is capable of robust accurate path following in the presence of wind disturbance. The algorithm is implemented in the flight control simulation of scaled YAK-54 research UAV; simulation test results are presented. These results demonstrate the effectiveness and performance of the proposed lateral guidance scheme.

关键词： Heuristic algorithms Algorithm design and analysis Robustness Vehicles Sliding mode control Xenon signal processing algorithms

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A machine learning system for automated whole-brain seizure detection

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applied Computing and Informatics 2016年第1期12卷 70-89页

作者： Fergus, P. Hussain, A. Hignett, David Al-Jumeily, D. Abdel-Aziz, Khaled Hamdan, Hani Applied Computing Research Group Liverpool John Moores University Byrom Street Liverpool L3 3AF United Kingdom The Walton Centre NHS Foundation Trust Lower Lane Fazakerley Liverpool L9 7LJ United Kingdom Supelec Department of Signal Processing and Electronic Systems Plateau de Moulon 3 rue Joliiot-Curie Gif-sur-Yvette Cedex 91192 France

Epilepsy is a chronic neurological condition that affects approximately 70 million people worldwide. Characterised by sudden bursts of excess electricity in the brain, manifesting as seizures, epilepsy is still not well understood when compared with other neurological disorders. Seizures often happen unexpectedly and attempting to predict them has been a research topic for the last 30 years. Electroencephalograms have been integral to these studies, as the recordings that they produce can capture the brain's electrical signals. The diagnosis of epilepsy is usually made by a neurologist, but can be difficult to make in the early stages. Supporting para-clinical evidence obtained from magnetic resonance imaging and electroencephalography may enable clinicians to make a diagnosis of epilepsy and instigate treatment earlier. However, electroencephalogram capture and interpretation is time consuming and can be expensive due to the need for trained specialists to perform the interpretation. Automated detection of correlates of seizure activity generalised across different regions of the brain and across multiple subjects may be a solution. This paper explores this idea further and presents a supervised machine learning approach that classifies seizure and non-seizure records using an open dataset containing 342 records (171 seizures and 171 non-seizures). Our approach posits a new method for generalising seizure detection across different subjects without prior knowledge about the focal point of seizures. Our results show an improvement on existing studies with 88% for sensitivity, 88% for specificity and 93% for the area under the curve, with a 12% global error, using the k-NN classifier. © 2015 The Authors

关键词： Classification Electroencephalogram Machine learning Non-seizure Oversampling Seizure

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Acoustic emission sonification and magnetic resonance imaging-based kinematics for exploratory analysis of knee joints

Acoustic emission sonification and magnetic resonance imagin...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lik-Kwan Shark Kai Huang Wei Quan John C. Waterton Michael A. Bowes John Goodacre Applied Digital Signal & Image Processing Research Centre University of Central Lancashire Preston UK Imorphics Ltd Manchester Science Park Manchester UK Biomedical Imaging Institute University of Manchester Manchester UK Lancaster Health Hub Lancaster University Lancaster UK

ISBN: (纸本)9781509016129

By combining two different measurement modalities, namely, acoustic emission (AE) and magnetic resonance imaging (MRI), this paper presents a novel auditory-visual software environment for exploratory analysis of knee joints. Using this tool, it becomes possible to listen to AE that is sonified to indicate surface friction among internal joint components during movement, examine patient-specific knee kinematics constructed from MRI to visualize surface contact areas between internal joint components during movement, and conduct integrated analysis of a knee joint by interactive and synchronized playback of sonified AE and animated joint movement, as well as perform comparative analysis of two different knee joints by using stereo sound and simultaneous display.

关键词： Magnetic analysis Magnetic resonance Bandwidth Kinematics Friction image edge detection Biomedical imaging

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Compressive Sampling for Spectral Imaging

Compressive Sampling for Spectral Imaging

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Conference on Lasers and Electro-Optics Europe

作者： L. Maidment A. Polak S. Marshall D. T. Reid Scottish Universities Physics Alliance (SUPA) Institute of Photonics and Quantum Sciences School of Engineering and Physical Sciences Heriot-Watt University Edinburgh EH14 4AS UK Centre for Signal & Image Processing University of Strathclyde 204 George Street Glasgow G1 1XW UK Fraunhofer Centre for Applied Photonics Fraunhofer UK Research Ltd 99 George Street Glasgow G1 1RD UK

ISBN: (纸本)9781509067374

Active spectral imaging in the mid-infrared can reveal absorption 'fingerprints' of different chemicals within a scene. The practicality of spectral imaging with active illumination is limited by the large number of images required (as the illumination wavelength or an interferometer mirror is scanned). Single pixel imaging uses the intensity reading from a single pixel detector while illuminating the scene with a series of speckle patterns, and reconstructs the image using a projection algorithm [1]. We have followed this approach, using an algorithm to reconstruct spectral information, rather than spatial.

关键词： Fourier transforms Ghost imaging Illumination Michelson interferometers Speckle patterns Spectral imaging

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Design of the ExCersion-VCI study: The effect of aerobic exercise on cerebral perfusion in patients with vascular cognitive impairment

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Alzheimer's and Dementia: Translational research and Clinical Interventions 2017年第2期3卷 157-165页

作者： Leeuwis, Anna E. Hooghiemstra, Astrid M. Amier, Raquel Ferro, Doeschka A. Franken, Leonie Nijveldt, Robin Kuijer, Joost P.A. Bronzwaer, Anne-Sophie G.T. van Lieshout, Johannes J. Rietberg, Marc B. Veerbeek, Janne M. Huijsmans, Rosalie J. Backx, Frank J.G. Teunissen, Charlotte E. Bron, Esther E. Barkhof, Frederik Prins, Niels D. Shahzad, Rahil Niessen, Wiro J. de Roos, Albert van Osch, Matthias J.P. van Rossum, Albert C. Biessels, Geert J. van der Flier, Wiesje M. Alzheimer Center and Department of Neurology Amsterdam Neuroscience VU University Medical Center Amsterdam Netherlands Department of Cardiology VU University Medical Center Amsterdam Netherlands Department of Neurology University Medical Center Utrecht Utrecht Netherlands Department of Physics and Medical Technology Amsterdam Neuroscience VU University Medical Center Amsterdam Netherlands Department of Internal Medicine Academic Medical Center University of Amsterdam Amsterdam Netherlands Laboratory for Clinical Cardiovascular Physiology Center for Heart Failure Research Academic Medical Center Amsterdam Netherlands MRC/ARUK Centre for Musculoskeletal Ageing Research School of Life Sciences The Medical School University of Nottingham United Kingdom Department of Rehabilitation Medicine MOVE Research Institute Amsterdam VU University Medical Center Amsterdam Netherlands Department of Rehabilitation Physical Therapy Science and Sport University Medical Centre Utrecht Utrecht Netherlands Neurochemistry Laboratory and Biobank Department of Clinical Chemistry Amsterdam Neuroscience VU University Medical Center Amsterdam Netherlands Biomedical Imaging Group Rotterdam Departments of Medical Informations and Radiology Erasmus MC Rotterdam Netherlands Department of Radiology and Nuclear Medicine Amsterdam Neuroscience VU University Medical Center Amsterdam Netherlands Institute of Neurology UCL London United Kingdom Institute of Healthcare Engineering UCL London United Kingdom Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Imaging Physics Applied Sciences Delft University of Technology Delft Netherlands Department of Radiology C.J. Gorter Center for high field MRI Leiden University Medical Center Leiden Netherlands Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Epidemiology VU University Medical Center Amsterdam Netherlands

There is evidence for a beneficial effect of aerobic exercise on cognition, but underlying mechanisms are unclear. In this study, we test the hypothesis that aerobic exercise increases cerebral blood flow (CBF) in patients with vascular cognitive impairment (VCI). This study is a multicenter single-blind randomized controlled trial among 80 patients with VCI. Most important inclusion criteria are a diagnosis of VCI with Mini-Mental State Examination ≥22 and Clinical Dementia Rating ≤0.5. Participants are randomized into an aerobic exercise group or a control group. The aerobic exercise program aims to improve cardiorespiratory fitness and takes 14 weeks, with a frequency of three times a week. Participants are provided with a bicycle ergometer at home. The control group receives two information meetings. Primary outcome measure is change in CBF. We expect this study to provide insight into the potential mechanism by which aerobic exercise improves hemodynamic status. © 2017 The Authors

关键词： Arterial spin labeling Cerebral blood flow Cognition Exercise Randomized clinical trial Vascular cognitive impairment

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Author Correction: The challenge of mapping the human connectome based on diffusion tractography

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Nature communications 2019年第1期10卷 5059页

作者： Klaus H Maier-Hein Peter F Neher Jean-Christophe Houde Marc-Alexandre Côté Eleftherios Garyfallidis Jidan Zhong Maxime Chamberland Fang-Cheng Yeh Ying-Chia Lin Qing Ji Wilburn E Reddick John O Glass David Qixiang Chen Yuanjing Feng Chengfeng Gao Ye Wu Jieyan Ma Renjie He Qiang Li Carl-Fredrik Westin Samuel Deslauriers-Gauthier J Omar Ocegueda González Michael Paquette Samuel St-Jean Gabriel Girard François Rheault Jasmeen Sidhu Chantal M W Tax Fenghua Guo Hamed Y Mesri Szabolcs Dávid Martijn Froeling Anneriet M Heemskerk Alexander Leemans Arnaud Boré Basile Pinsard Christophe Bedetti Matthieu Desrosiers Simona Brambati Julien Doyon Alessia Sarica Roberta Vasta Antonio Cerasa Aldo Quattrone Jason Yeatman Ali R Khan Wes Hodges Simon Alexander David Romascano Muhamed Barakovic Anna Auría Oscar Esteban Alia Lemkaddem Jean-Philippe Thiran H Ertan Cetingul Benjamin L Odry Boris Mailhe Mariappan S Nadar Fabrizio Pizzagalli Gautam Prasad Julio E Villalon-Reina Justin Galvis Paul M Thompson Francisco De Santiago Requejo Pedro Luque Laguna Luis Miguel Lacerda Rachel Barrett Flavio Dell'Acqua Marco Catani Laurent Petit Emmanuel Caruyer Alessandro Daducci Tim B Dyrby Tim Holland-Letz Claus C Hilgetag Bram Stieltjes Maxime Descoteaux Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. k.maier-hein@dkfz.de. Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. Sherbrooke Connectivity Imaging Lab (SCIL) Université de Sherbrooke Sherbrooke QC J1K 0A5 QC Canada. Department of Intelligent Systems Engineering School of Informatics and Computing Indiana University Bloomington IN 47408 USA. Krembil Research Institute University Health Network Toronto Canada M5G 2C4. Department of Neurological Surgery University of Pittsburgh School of Medicine Pittsburgh PA 15213 USA. IMT-Institute for Advanced Studies Lucca 55100 Italy. Department of Diagnostic Imaging St. Jude Children's Research Hospital Memphis TN 38105 USA. University of Toronto Institute of Medical Science Toronto Canada M5S 1A8. Institute of Information Processing and Automation Zhejiang University of Technology Hangzhou 310023 Zhejiang China. United Imaging Healthcare Co Shanghai 201807 China. Shanghai Advanced Research Institute Shanghai 201210 China. Laboratory of Mathematics in Imaging Harvard Medical School Boston MA 02215 USA. Center for Research in Mathematics Guanajuato 36023 Mexico. PROVIDI Lab Image Sciences Institute University Medical Center Utrecht Utrecht 3508 The Netherlands. Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University Maindy Road Cardiff CF24 4HQ UK. Department of Radiology University Medical Center Utrecht Utrecht 3508 The Netherlands. Centre de recherche institut universitaire de geriatrie de Montreal (CRIUGM) Université de Montréal Montreal QC Canada H3W 1W5. Sorbonne Universités UPMC Univ Paris 06 CNRS INSERM Laboratoire d'Imagerie Biomédicale (LIB) 75013 Paris France. Center for Advanced Research in Sleep Medicine Hôpital du Sacré-Coeur de Montréal Montreal Canada H4J 1C5. Neuroimaging Unit Institute of Bioimaging and Molecular Physiology (IBFM) Nat

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Author Correction: Brain clocks capture diversity and disparities in aging and dementia across geographically diverse populations

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Nature medicine 2024年第12期30卷 3779页

作者： Sebastian Moguilner Sandra Baez Hernan Hernandez Joaquín Migeot Agustina Legaz Raul Gonzalez-Gomez Francesca R Farina Pavel Prado Jhosmary Cuadros Enzo Tagliazucchi Florencia Altschuler Marcelo Adrián Maito María E Godoy Josephine Cruzat Pedro A Valdes-Sosa Francisco Lopera John Fredy Ochoa-Gómez Alfredis Gonzalez Hernandez Jasmin Bonilla-Santos Rodrigo A Gonzalez-Montealegre Renato Anghinah Luís E d'Almeida Manfrinati Sol Fittipaldi Vicente Medel Daniela Olivares Görsev G Yener Javier Escudero Claudio Babiloni Robert Whelan Bahar Güntekin Harun Yırıkoğulları Hernando Santamaria-Garcia Alberto Fernández Lucas David Huepe Gaetano Di Caterina Marcio Soto-Añari Agustina Birba Agustin Sainz-Ballesteros Carlos Coronel-Oliveros Amanuel Yigezu Eduar Herrera Daniel Abasolo Kerry Kilborn Nicolás Rubido Ruaridh A Clark Ruben Herzog Deniz Yerlikaya Kun Hu Mario A Parra Pablo Reyes Adolfo M García Diana L Matallana José Alberto Avila-Funes Andrea Slachevsky María I Behrens Nilton Custodio Juan F Cardona Pablo Barttfeld Ignacio L Brusco Martín A Bruno Ana L Sosa Ortiz Stefanie D Pina-Escudero Leonel T Takada Elisa Resende Katherine L Possin Maira Okada de Oliveira Alejandro Lopez-Valdes Brian Lawlor Ian H Robertson Kenneth S Kosik Claudia Duran-Aniotz Victor Valcour Jennifer S Yokoyama Bruce Miller Agustin Ibanez Latin American Brain Health Institute Universidad Adolfo Ibañez Santiago de Chile Chile. Cognitive Neuroscience Center Universidad de San Andrés Buenos Aires Argentina. Department of Neurology Massachusetts General Hospital and Harvard Medical School Boston MA USA. Universidad de los Andes Bogota Colombia. Global Brain Health Institute (GBHI) University of California San Francisco CA USA. Global Brain Health Institute (GBHI) Trinity College Dublin Dublin Ireland. The University of California Santa Barbara (UCSB) Santa Barbara CA USA. Escuela de Fonoaudiología Universidad San Sebastián Santiago de Chile Chile. Grupo de Bioingeniería Decanato de Investigación Universidad Nacional Experimental del Táchira San Cristóbal Venezuela. Advanced Center for Electrical and Electronic Engineering Universidad Técnica Federico Santa María Valparaíso Chile. University of Buenos Aires Buenos Aires Argentina. The Clinical Hospital of Chengdu Brain Sciences Institute University of Electronic Sciences and Technology of China Chengdu China. Technology of China Chengdu China. Cuban Neuroscience Center La Habana Cuba. Grupo de Neurociencias de Antioquia (GNA) University of Antioquia Medellín Colombia. Department of Psychology Master Program of Clinical Neuropsychology Universidad Surcolombiana Neiva Neiva Colombia. Department of Psychology Universidad Cooperativa de Colombia Arauca Colombia. Neurocognition and Psychophysiology Laboratory Universidad Surcolombiana Neiva Colombia. Reference Center of Behavioural Disturbances and Dementia School of Medicine University of Sao Paulo Sao Paulo Brazil. Traumatic Brain Injury Cognitive Rehabilitation Out-Patient Center University of Sao Paulo Sao Paulo Brazil. Center for Social and Cognitive Neuroscience School of Psychology Universidad Adolfo Ibáñez Santiago Chile. Neuropsychology and Clinical Neuroscience Laboratory (LANNEC) Physiopathology Program-Institute of Biomedical Sciences (ICBM) Neuroscience and East Neuroscience Dep

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